Diagnosis and prediction of business process deviances. This project aims to develop an innovative approach based on process execution semantics, to analyse event data logged by IT systems in order to diagnose and predict business process deviance. Anticipated outcomes include novel business intelligence algorithms producing deviance diagnostics, predictions and recommendations and exposing results via interactive visual analytics. The outcomes are expected to aid process workers in steering bus ....Diagnosis and prediction of business process deviances. This project aims to develop an innovative approach based on process execution semantics, to analyse event data logged by IT systems in order to diagnose and predict business process deviance. Anticipated outcomes include novel business intelligence algorithms producing deviance diagnostics, predictions and recommendations and exposing results via interactive visual analytics. The outcomes are expected to aid process workers in steering business operations towards consistent and compliant outcomes and higher performance, and assist analysts and auditors to explain deviant operations. This should significantly benefit industries such as healthcare, insurance, retail and the government where compliance and integrity management are imperative.Read moreRead less
Embedding Enterprise Systems in IoT Fog Networks through Microservices. The project will enable automated re-engineering of enterprise systems, to allow them to reused in Internet-of-Things (IoT) applications. It will support efficient ways in which the core business logic of these large scale and monolithic systems can be extended into resource control and data sensing functions managed through the IoT. The project will develop a novel, fine-grained software architecture style suitable for loca ....Embedding Enterprise Systems in IoT Fog Networks through Microservices. The project will enable automated re-engineering of enterprise systems, to allow them to reused in Internet-of-Things (IoT) applications. It will support efficient ways in which the core business logic of these large scale and monolithic systems can be extended into resource control and data sensing functions managed through the IoT. The project will develop a novel, fine-grained software architecture style suitable for localised IoT execution, through microservices executing autonomously on nodes of IoT fog networks. It will develop new techniques for automated discovery of microservices from enterprise systems and the verification of future-state system execution based on current-state behavioural and other properties such as security.Read moreRead less
Re-engineering enterprise systems for microservices in the cloud. This project will enable automatic re-engineering of large enterprise applications to run in modern cloud environments as microservices. Microservices are the latest wave of service-based software, capable of exploiting the high performance and third-party integration opportunities made available through the cloud. The project will develop new techniques for analysing enterprise systems code and execution data, and making recommen ....Re-engineering enterprise systems for microservices in the cloud. This project will enable automatic re-engineering of large enterprise applications to run in modern cloud environments as microservices. Microservices are the latest wave of service-based software, capable of exploiting the high performance and third-party integration opportunities made available through the cloud. The project will develop new techniques for analysing enterprise systems code and execution data, and making recommendations for restructuring suitable parts as microservices. These microservices manage individual business objects via sets of lightweight distributed computational operations. The outcomes will support progressive evolution of an enterprise system, into distributed microservices running in public clouds, while still being integrated with "backend" systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101465
Funder
Australian Research Council
Funding Amount
$419,498.00
Summary
Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods emp ....Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods empowering a Web-based observatory dashboard of fake news propagation. This achieves significant benefits for media organisations, governments, the public, and academia via timely alerts, data-journalism reports, and novel data visualisations of social media landscape to distinguish between legitimate and deceptive contents.Read moreRead less
Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-ti ....Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-time recommendations. This project will devise a series of cost-effective machine learning methods and schemes to deliver an end-to-end recommender framework. This project has the potential to significantly reduce the energy consumption of large-scale recommender systems as well as facilitating an increase in the use of recommendation applications for big data.Read moreRead less